Why in news?
Generative artificial intelligence exploded in popularity through tools like ChatGPT and Gemini. By late 2025 researchers and regulators were examining both the promise of large language models (LLMs) and their shortcomings. New studies highlighted ways to improve reasoning efficiency and warned about the risk of “model collapse” when models train on AI‑generated data.
What is a large language model?
A large language model is a type of artificial intelligence trained on massive collections of text and other symbolic data. It uses a deep neural‑network architecture called a transformer to learn patterns and relationships between words. By predicting the most likely next word in a sequence, the model can generate coherent text, translate languages, summarise documents, write code and answer questions.
How LLMs are trained and used
- Training data: Models ingest billions of words from books, websites, scientific papers, forums, programming code and other sources. The more diverse the data, the broader the model’s vocabulary and knowledge.
- Parameters: The model’s “knowledge” is encoded in parameters – numerical values within the neural network. Frontier models can have tens or hundreds of billions of parameters.
- Applications: Chatbots, digital assistants, translation services, personalised tutoring systems, content generation and software development all use LLMs. They can also analyse legal and medical documents, but caution is needed in high‑stakes domains.
Opportunities
- LLMs enable natural interaction with computers, lowering language barriers and democratising information access.
- They automate routine tasks such as drafting emails or summarising reports, freeing humans for more creative work.
- Specialised models trained on local languages and domains can support education, healthcare and agriculture in India.
Limitations and emerging concerns
- Hallucinations: Models sometimes generate plausible‑sounding but incorrect answers because they lack true understanding of facts.
- Bias and fairness: Training data reflect social biases. Without careful curation, models may reproduce harmful stereotypes or misinformation.
- Energy use: Training and running large models require significant computing power and energy, raising environmental concerns.
- Model collapse: A 2024 study in Nature showed that when new models are trained on text generated by earlier models, rare patterns in language can disappear. Over successive generations the model’s outputs converge towards bland averages, losing diversity and accuracy. Retaining a portion of genuine human‑written data in training helps mitigate this.
- Reasoning efficiency: Researchers at MIT proposed an adaptive approach allowing LLMs to allocate more computation to difficult questions and less to simple ones. This technique improves accuracy while reducing energy use.
Looking ahead
- Developers are working on multilingual and multimodal models that handle images, audio and video alongside text.
- Regulators are drafting rules to ensure transparency, accountability and safety in AI applications.
- Responsible deployment in India will require local datasets, robust evaluation and public awareness about both benefits and risks.
Sources: TH
